AlphaFold and Global Knowledge Inequality: How AI for Science Exacerbates the Research Divide

This article focuses on AlphaFold, an AI4S tool, and explores its unequal effects in global knowledge production. Although AlphaFold has made breakthroughs in protein structure prediction and opened access to its results, technological dividends are skewed toward developed countries, exacerbating the research divide. The article analyzes the current state of inequality, empirical evidence, underlying causes, and response strategies, and reflects on the contradictions and future directions in AI4S development.

In 2021, DeepMind released AlphaFold2, solving the 50-year-old problem of protein structure prediction in biology. In collaboration with EMBL-EBI, it opened access to over 200 million protein structure predictions. Its open access policy theoretically benefits scientists worldwide, but "access does not equal usability"—real benefits require supporting infrastructure, computing resources, and professional capabilities.

Global research resources are highly concentrated in a few developed countries: North America and Western Europe account for nearly 60% of global R&D spending, while sub-Saharan Africa accounts for only 0.5%. There are three major barriers to AI4S: computing power (GPU clusters, cloud computing cost burdens), data (lack of high-quality data, sharing barriers), and skills (shortage of interdisciplinary talents, brain drain).

Research through bibliometrics, citation network, and collaboration network analysis found: 1. Research institutions in developed countries have faster growth in paper output using AlphaFold; 2. The Matthew effect is evident—leading institutions are more likely to integrate new tools; 3. Developing countries are mostly collaborators rather than leaders; 4. The right to set research agendas is concentrated in developed countries.

Structural factors: infrastructure gaps (insufficient internet, electricity, computing facilities), institutional barriers (Western-centric research evaluation systems, imperfect intellectual property rights), cultural and language barriers (predominance of English resources). Technical characteristic factors: model black-boxing (developing countries are mostly users rather than innovators), data colonialism (local data is analyzed by developed countries).

International level: Strengthen technology transfer and capacity building, reform global research governance; National level: Invest in digital infrastructure, cultivate local interdisciplinary talents; Institutional level: Establish regional collaboration networks, promote open science practices.

The AlphaFold case reveals the contradiction of AI4S: technology is neutral, but benefit distribution is influenced by social structures. Proactive interventions are needed in the future: ensure technology inclusivity, diverse participation in research agendas, and long-term investment in infrastructure and education. AI4S should move toward a direction where all humanity shares the dividends of scientific progress.